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Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

Edited by
September 2022
196 pages
  • ISBN978-3-0365-5393-1 (Hardback)
  • ISBN978-3-0365-5394-8 (PDF)
https://doi.org/10.3390/books978-3-0365-5394-8
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This book is a reprint of the Special Issue Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods that was published in

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Chemistry & Materials Science
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Environmental & Earth Sciences
Physical Sciences
Summary

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods.

Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks.

The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
data hiding; AMBTC; BTC; Hamming code; LSB; predicate encryption; inner product encryption; constant-size private key; efficient decryption; constant pairing computations; watermarking; self-embedding; digital signature; AMBTC; fragile watermarking; constrained backtracking matching pursuit; sparse reconstruction; compressed sensing; greedy pursuit algorithm; image processing; visual surveillance; deep learning; object detection; object detection; latency optimization; mobile edge cloud; connected autonomous cars; smart city; video surveillance; physical layer security; secure transmission; secrecy capacity; secrecy capacity optimization artificial noise; power allocation; channel estimation error; neural network; transfer learning; scalograms; MFCC; Log-mel; pre-trained models; seismic patch classification; CNN-features; transfer learning; data complexity; handwritten text recognition; Residual Network; Transformer model; object detection; named entity recognition; n/a